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Forecasting the evolution of contagion dynamics is still an open problem towhich mechanistic models only offer a partial answer. To remain mathematicallyor computationally tractable, these models must rely on simplifyingassumptions, thereby limiting the quantitative accuracy of their predictionsand the complexity of the dynamics they can model. Here, we propose acomplementary approach based on deep learning where the effective localmechanisms governing a dynamic on a network are learned from time series data.Our graph neural network architecture makes very few assumptions about thedynamics, and we demonstrate its accuracy using different contagion dynamics ofincreasing complexity. By allowing simulations on arbitrary network structures,our approach makes it possible to explore the properties of the learneddynamics beyond the training data. Finally, we illustrate the applicability ofour approach using real data of the COVID-19 outbreak in Spain. Our resultsdemonstrate how deep learning offers a new and complementary perspective tobuild effective models of contagion dynamics on networks.

Deep learning of contagion dynamics on complex networks